BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//conferences.acspri.org.au//2022//speaker//8AVLLH BEGIN:VTIMEZONE TZID:AEST BEGIN:STANDARD DTSTART:20000326T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3;UNTIL=20050326T170000Z TZNAME:AEST TZOFFSETFROM:+1100 TZOFFSETTO:+1000 END:STANDARD BEGIN:STANDARD DTSTART:20060402T040000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4;UNTIL=20060401T170000Z TZNAME:AEST TZOFFSETFROM:+1100 TZOFFSETTO:+1000 END:STANDARD BEGIN:STANDARD DTSTART:20070325T040000 RRULE:FREQ=YEARLY;BYDAY=4SU;BYMONTH=3;UNTIL=20070324T170000Z TZNAME:AEST TZOFFSETFROM:+1100 TZOFFSETTO:+1000 END:STANDARD BEGIN:STANDARD DTSTART:20080406T040000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4 TZNAME:AEST TZOFFSETFROM:+1100 TZOFFSETTO:+1000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000827T030000 RRULE:FREQ=YEARLY;BYDAY=4SU;BYMONTH=8;UNTIL=20000826T170000Z TZNAME:AEDT TZOFFSETFROM:+1000 TZOFFSETTO:+1100 END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20011028T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10;UNTIL=20071027T170000Z TZNAME:AEDT TZOFFSETFROM:+1000 TZOFFSETTO:+1100 END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20081005T030000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=10 TZNAME:AEDT TZOFFSETFROM:+1000 TZOFFSETTO:+1100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-2022-CLCYD9@conferences.acspri.org.au DTSTART;TZID=AEST:20221123T132000 DTEND;TZID=AEST:20221123T133500 DESCRIPTION:The Likert scale enjoys common usage in fsQCA as a standardised tool for operationalising conceptual models (e.g. TAM\, UTAUT) and collec ting quantitative data to be calibrated into fuzzy scores. However\, the l imited discrete options can restrict the nuance of responses which may lie in-between or beyond provided answers. Respondents may select options tha t do not accurately reflect the magnitude of their views and there is an i ncreased risk of the ceiling effect causing limited data variation. Theore tical concerns exist with using direct algorithmic calibration (involving supply of qualitative anchors for defining values constituting full member ship\, full nonmembership\, and cross-over point) on ordinal measured data from Likert scales as it is intended for interval and ratio data. The sli der scale is proposed as a more robust instrument leveraging set-theoretic and fuzzy logic principles for use in fsQCA.\n\nThe slider scale is a con tinuous rating scale for measuring ratio data (spanning from 0 to 100 in i ncrements of one) whereby respondents drag a digital marker along a horizo ntal quantitative scale to indicate their response. Qualitative descriptor s are distributed across the slider scale in a predetermined order as anch ors covering different ranges of raw values and signposting different leve ls of membership scores. These anchors comprise a rubric description that provides respondents with a clear criteria to self-assess their degree of membership in some variable of interest and reduces uncertainty around how the scale is interpreted. This is useful for variables measurable as sing le-item scales or efficiently aggregating multiple items measuring the sam e variable dimension into one single item. The broader response continuum enables respondents to express their answers with greater precision and gr anularity\, facilitating nuanced differentiation between membership scores . Notably\, scores and qualitative anchors are easily mappable to ordinal scales to accommodate larger-N studies. \n\nStudies have shown the measure ment quality of slider scales are comparable to the reliability and validi ty of Likert scales. Slider scales can be used without materially compromi sing data quality\, are less susceptible to the ceiling effect\, and are m ore likely to yield normally distributed values. Importantly\, there is th e risk of systematic measurement error caused by the starting position of the digital marker and higher non-response rates associated with greater e ffort required to answer questions. These can easily be addressed through careful survey design and piloting feedback.\n\nThe ratio level of measure ment of the slider scale makes it suitable for direct calibration and avoi ds any theoretical issues with using ordinal data from Likert scales. Raw values within the scale range of 0 to 100\, representing the progression f rom full nonmembership to full membership\, naturally captures the monoton ic property of fuzzy sets ranging from 0 to 1 when transformed by some log istic function. The resulting set membership scores and supplied anchors ( whether using variable distribution or substantive criteria) will be more fine-grained from using numeric over categorical data. This reasoning furt her applies to manual and indirect calibration.\n\nA concrete application of the slider scale is presented as part of a pilot study investigating th e causal complexity underpinning clinician acceptability of an artificial intelligence-based diagnostic support tool in real-world medical practice. The findings demonstrate how it can operationalise conceptual models and facilitate set-theoretic\, configurational analysis particularly for explo ratory research where dimensionality is high and sample size is low.\n\nAu thors:\n- Mr David Hua - dhua9758@uni.sydney.edu.au\n- Dr Neysa Petrina - neysa.petrina@sydney.edu.au\n- Dr Simon Poon - simon.poon@sydney.edu.au\n\ nRecording link: https://acspri-org-au.zoom.us/rec/share/bMvRMHyt1mxBmr3v9 JEMtzw40XMlR7ie_q-pDBV0BwdJ1Nf47TlmI5mkRiBW5d4n.4ITWOzq9QFTcwMgN?startTime =1669170286000 DTSTAMP:20260809T220300Z LOCATION:Zoom Breakout Room 2 SUMMARY:Using slider scales for fuzzy-set qualitative comparative analysis (fsQCA): a fuzzy-set-theoretic approach to measuring degrees of membership - David Hua URL:https://conferences.acspri.org.au/2022/talk/CLCYD9/ END:VEVENT END:VCALENDAR